25 citations · 66 across the 16 of their papers we have counts for
19 papers
Gibbs optimal design of experiments
Antony M. Overstall, Jacinta Holloway-Brown, James M. McGree
Bayesian optimal design is a well-established approach to planning experiments. A distribution for the responses, i.e. a statistical model, is assumed which is dependent on unknown…
Zero-Truncated Modelling in a Meta-Analysis on Suicide Data after Bariatric Surgery
Layna Charlie Dennett, Antony Overstall, Dankmar Boehning
Meta-analysis is a well-established method for integrating results from several independent studies to estimate a common quantity of interest. However, meta-analysis is prone to se…
A general Bayesian approach to design adaptive clinical trials with time-to-event outcomes
James M. McGree, Antony M. Overstall, Mark Jones +1
Clinical trials are an integral component of medical research. Trials require careful design to, for example, maintain the safety of participants, use resources efficiently and all…
Robust simulation design for generalized linear models in conditions of heteroscedasticity or correlation
Andrew Gill, David J. Warne, Antony M. Overstall +2
A meta-model of the input-output data of a computationally expensive simulation is often employed for prediction, optimization, or sensitivity analysis purposes. Fitting is enabled…
Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
Damianos Michaelides, Maria Adamou, David C. Woods +1
A Bayesian optimal experimental design framework is developed for experiments where settings of one or more variables, referred to as profile variables, can be functions. For this…
General Bayesian L2 calibration of mathematical models
Antony M. Overstall, James M. McGree
A mathematical model is a function taking certain arguments and returning a theoretical prediction of a feature of a physical system. The arguments to the mathematical model can be…
Properties of using Fisher information gain for Bayesian design of experiments
Antony M. Overstall
The Bayesian decision-theoretic approach to design of experiments involves specifying a design (values of all controllable variables) to maximise the expected utility function (exp…
Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects
S. G. Jagath Senarathne, Antony M. Overstall, James M. McGree
This article proposes a novel adaptive design algorithm that can be used to find optimal treatment allocations in N-of-1 clinical trials. This new methodology uses two Laplace appr…
Bayesian decision-theoretic design of experiments under an alternative model
Antony M. Overstall, James M. McGree
Traditionally Bayesian decision-theoretic design of experiments proceeds by choosing a design to minimise expectation of a given loss function over the space of all designs. The lo…
A synthetic likelihood-based Laplace approximation for efficient design of biological processes
Mahasen Dehideniya, Antony M. Overstall, Chris C. Drovandi +1
Complex models used to describe biological processes in epidemiology and ecology often have computationally intractable or expensive likelihoods. This poses significant challenges…
Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation
Antony M. Overstall, James M. McGree
A Bayesian design is given by maximising an expected utility over a design space. The utility is chosen to represent the aim of the experiment and its expectation is taken with res…
acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange
Antony Overstall, David Woods, Maria Adamou
We describe the R package acebayes and demonstrate its use to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with the optimal design maximisi…
Quantifying Uncertainty in Transdimensional Markov Chain Monte Carlo Using Discrete Markov Models
Daniel W. Heck, Antony M. Overstall, Quentin F. Gronau +1
Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evalua…
An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions
Antony M. Overstall, James M. McGree, Christopher C. Drovandi
The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically intractable expected loss func…
Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application
David C. Woods, Antony M. Overstall, Maria Adamou +1
The design of an experiment can be always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and…
Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics
Antony Overstall, David Woods, Kieran Martin
Quality control in industrial processes is increasingly making use of prior scientific knowledge, often encoded in physical models that require numerical approximation. Statistical…
Bayesian optimal design for ordinary differential equation models with application in biological science
Antony Overstall, David Woods, Ben Parker
Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of non-linear ordinary differential equations. The motivat…
Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model
Antony Overstall, David Woods
We present a common framework for Bayesian emulation methodologies for multivariate-output simulators, or computer models, that employ either parametric linear models or nonparamet…
Bayesian Design of Experiments using Approximate Coordinate Exchange
Antony Overstall, David Woods
The construction of decision-theoretic Bayesian designs for realistically-complex nonlinear models is computationally challenging, as it requires the optimization of analytically i…